'''simple docstring'''




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__UpperCAmelCase					="bert-base-cased"
__UpperCAmelCase					="google/pegasus-xsum"
__UpperCAmelCase					=[" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCAmelCase					=["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCAmelCase					="patrickvonplaten/t5-tiny-random"
__UpperCAmelCase					="sshleifer/bart-tiny-random"
__UpperCAmelCase					="sshleifer/tiny-mbart"
__UpperCAmelCase					="sshleifer/tiny-marian-en-de"






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[int]:
    __lowerCamelCase            =    '''\n'''.join(UpperCamelCase__							)
    Path(UpperCamelCase__							).open('''w'''							).writelines(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCamelCase__							,    f"""{split}.source"""							)							,    UpperCamelCase__							)
        _dump_articles(os.path.join(UpperCamelCase__							,    f"""{split}.target"""							)							,    UpperCamelCase__							)
    return tmp_dir




class        a__ (  UpperCAmelCase__					):






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,							)
     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoTokenizer.from_pretrained(a       )
         __lowerCamelCase            =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         __lowerCamelCase            =    max(len(tokenizer.encode(a       )       ) for a in ARTICLES       )
         __lowerCamelCase            =    max(len(tokenizer.encode(a       )       ) for a in SUMMARIES       )
         __lowerCamelCase            =    4
         __lowerCamelCase            =    8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         __lowerCamelCase      ,    __lowerCamelCase            =    '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
         __lowerCamelCase            =    SeqaSeqDataset(
             a    ,							data_dir=a    ,							type_path='''train'''    ,							max_source_length=a    ,							max_target_length=a    ,							src_lang=a    ,							tgt_lang=a    ,							)
         __lowerCamelCase            =    DataLoader(a    ,							batch_size=2    ,							collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert isinstance(a    ,							a       )
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_src_len
             # show that targets are the same len
             assert batch["labels"].shape[1] == max_tgt_len
             if tok_name != MBART_TINY:
                 continue
             # check language codes in correct place
             __lowerCamelCase            =    shift_tokens_right(batch['''labels''']    ,							tokenizer.pad_token_id       )
             assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
             assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

             break  # No need to test every batch






     @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoTokenizer.from_pretrained(a       )
         __lowerCamelCase            =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         __lowerCamelCase            =    max(len(tokenizer.encode(a       )       ) for a in ARTICLES       )
         __lowerCamelCase            =    max(len(tokenizer.encode(a       )       ) for a in SUMMARIES       )
         __lowerCamelCase            =    4
         __lowerCamelCase            =    LegacySeqaSeqDataset(
             a    ,							data_dir=a    ,							type_path='''train'''    ,							max_source_length=20    ,							max_target_length=a    ,							)
         __lowerCamelCase            =    DataLoader(a    ,							batch_size=2    ,							collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_len_source
             assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
             # show that targets were truncated
             assert batch["labels"].shape[1] == trunc_target  # Truncated
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

         __lowerCamelCase            =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         __lowerCamelCase            =    tmp_dir.joinpath('''train.source'''       ).open().readlines()
         __lowerCamelCase            =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(a    ,							a    ,							1_28    ,							a       )
         __lowerCamelCase            =    {x.name for x in tmp_dir.iterdir()}
         __lowerCamelCase            =    {x.name for x in save_dir.iterdir()}
         __lowerCamelCase            =    save_dir.joinpath('''train.source'''       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(a       ) < len(a       )
         assert len(a       ) == 1
         assert len(packed_examples[0]       ) == sum(len(a       ) for x in orig_examples       )
         assert orig_paths == new_paths






     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,							reason='''This test requires fairseq'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         if not FAIRSEQ_AVAILABLE:
             return
         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_dataset(max_len=64       )
         __lowerCamelCase            =    64
         __lowerCamelCase            =    ds.make_dynamic_sampler(a    ,							required_batch_size_multiple=a       )
         __lowerCamelCase            =    [len(a       ) for x in batch_sampler]
         assert len(set(a       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(a       ) == len(a       )  # no dropped or added examples
         __lowerCamelCase            =    DataLoader(a    ,							batch_sampler=a    ,							collate_fn=ds.collate_fn    ,							num_workers=2       )
         __lowerCamelCase            =    []
         __lowerCamelCase            =    []
         for batch in data_loader:
             __lowerCamelCase            =    batch['''input_ids'''].shape
             __lowerCamelCase            =    src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             __lowerCamelCase            =    np.product(batch['''input_ids'''].shape       )
             num_src_per_batch.append(a       )
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(a       )
         assert num_src_per_batch[0] == max(a       )
         if failures:
             raise AssertionError(f"""too many tokens in {len(a       )} batches"""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_dataset(max_len=5_12       )
         __lowerCamelCase            =    2
         __lowerCamelCase            =    ds.make_sortish_sampler(a    ,							shuffle=a       )

         __lowerCamelCase            =    DataLoader(a    ,							batch_size=a    ,							collate_fn=ds.collate_fn    ,							num_workers=2       )
         __lowerCamelCase            =    DataLoader(a    ,							batch_size=a    ,							collate_fn=ds.collate_fn    ,							num_workers=2    ,							sampler=a       )

         __lowerCamelCase            =    tokenizer.pad_token_id

         def count_pad_tokens(a						:						Any    ,							a						:						List[Any]="input_ids"       ):
             return [batch[k].eq(a       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(a    ,							k='''labels'''       )       ) < sum(count_pad_tokens(a    ,							k='''labels'''       )       )
         assert sum(count_pad_tokens(a       )       ) < sum(count_pad_tokens(a       )       )
         assert len(a       ) == len(a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Dict=10_00    ,							a						:						int=1_28       ):





         """simple docstring"""



         if os.getenv('''USE_REAL_DATA'''    ,							a       ):
             __lowerCamelCase            =    '''examples/seq2seq/wmt_en_ro'''
             __lowerCamelCase            =    max_len * 2 * 64
             if not Path(a       ).joinpath('''train.len'''       ).exists():
                 save_len_file(a    ,							a       )
         else:
             __lowerCamelCase            =    '''examples/seq2seq/test_data/wmt_en_ro'''
             __lowerCamelCase            =    max_len * 4
             save_len_file(a    ,							a       )

         __lowerCamelCase            =    AutoTokenizer.from_pretrained(a       )
         __lowerCamelCase            =    SeqaSeqDataset(
             a    ,							data_dir=a    ,							type_path='''train'''    ,							max_source_length=a    ,							max_target_length=a    ,							n_obs=a    ,							)
         return ds, max_tokens, tokenizer






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._get_dataset()
         __lowerCamelCase            =    set(DistributedSortishSampler(a    ,							2_56    ,							num_replicas=2    ,							rank=0    ,							add_extra_examples=a       )       )
         __lowerCamelCase            =    set(DistributedSortishSampler(a    ,							2_56    ,							num_replicas=2    ,							rank=1    ,							add_extra_examples=a       )       )
         assert idsa.intersection(a       ) == set()






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,							)
     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoTokenizer.from_pretrained(a    ,							use_fast=a       )
         if tok_name == MBART_TINY:
             __lowerCamelCase            =    SeqaSeqDataset(
                 a    ,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,							type_path='''train'''    ,							max_source_length=4    ,							max_target_length=8    ,							src_lang='''EN'''    ,							tgt_lang='''FR'''    ,							)
             __lowerCamelCase            =    train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             __lowerCamelCase            =    SeqaSeqDataset(
                 a    ,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,							type_path='''train'''    ,							max_source_length=4    ,							max_target_length=8    ,							)
             __lowerCamelCase            =    train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(a       ) == 1 if tok_name == BART_TINY else len(a       ) == 0

'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
